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ABSTRACT Deep Sequence-to-sequence models have rapidly become an indispensable general-purpose tool for many applications in natural language processing, such as machine translation, summarization, and dialogue. Many problems that once required careful domain-specific engineering can now be tackled using off-the-shelf systems by interested tinkerers. However, even with the evident early success of these models, the seq2seq framework itself is still relatively unexplored. In this talk, I will discuss three questions we have been studying in the area of sequence-to-sequence NLP: (1) Can we interpret seq2seq's learned representations? [Strobelt et al, 2016], (2) How should a seq2seq model be trained? [Wiseman and Rush, 2016], (3) How many parameters are necessary for the models to work? [Kim and Rush, 2016]. Along the way, I will present applications in summarization, grammar correction, image-to-text, and machine translation (on your phone).

BIOAlexander Rush is an Assistant Professor at Harvard University studying NLP, and formerly a Post-doc at Facebook Artificial Intelligence Research (FAIR). He is interested in machine learning and deep learning methods for large-scale natural language processing and understanding. His past work has introduced novel methods for structured prediction with applications to syntactic parsing and machine translation. His group web page is at nlp.seas.harvard.edu and he tweets at twitter.com/harvardnlp.